Implementation of Universal Neural Network Approximator on a ULP Microcontroller for Wavelet Synthesis in Electroencephalography
| Autor: | Ivan A. Bogoslovskii, Albina V. Pomogalova, Daniil V. Ermolenko, Klavdiya Kh. Kilicheva, Andrey B. Stepanov |
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| Rok vydání: | 2019 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
medicine.diagnostic_test Artificial neural network Computer science business.industry Computer Science::Neural and Evolutionary Computation Clock rate Approximation algorithm Electroencephalography Microcontroller ComputingMethodologies_PATTERNRECOGNITION Wavelet Multilayer perceptron medicine Artificial intelligence business Continuous wavelet transform |
| Zdroj: | 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). |
| DOI: | 10.1109/eiconrus.2019.8657259 |
| Popis: | The paper describes the implementation of universal neural network approximator on microcontroller MSP430G2553 with ultralow power consumption. A multilayer perceptron is used as an approximating artificial neural network. Approximation is one of the stages of building wavelet neural network models. The approximation of electroencephalogram fragments allows obtaining wavelets adapted for its continuous wavelet transform. Implementation of neural network approximator on this microcontroller is one of the stages to create a portable system for automated electroencephalogram analysis. Two ways of implementation a neural network approximator are presented in the paper. The first way is based on the experiment with the neural network approximator without its training. The second way involves the experiment based on the interaction with the neural network approximator with training algorithm. The studies of the approximation rate depending on the number of iterations and the core clock frequency of the microcontroller have been conducted. |
| Databáze: | OpenAIRE |
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